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Recognising realistic emotions and affect in speech: State of the art and lessons learnt from the first challenge.

, , , and . Speech Communication, 53 (9-10): 1062-1087 (2011)

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The TUM Gait from Audio, Image and Depth (GAID) database: Multimodal recognition of subjects and traits., , , , and . J. Visual Communication and Image Representation, 25 (1): 195-206 (2014)Being bored? Recognising natural interest by extensive audiovisual integration for real-life application., , , , , , , , and . Image Vision Comput., 27 (12): 1760-1774 (2009)Applying multiple classifiers and non-linear dynamics features for detecting sleepiness from speech., , , , and . Neurocomputing, (2012)Data-driven clustering in emotional space for affect recognition using discriminatively trained LSTM networks., , , , and . INTERSPEECH, page 1595-1598. ISCA, (2009)Robust in-car spelling recognition - a tandem BLSTM-HMM approach., , , , , and . INTERSPEECH, page 2507-2510. ISCA, (2009)The INTERSPEECH 2012 Speaker Trait Challenge., , , , , , , , , and 2 other author(s). INTERSPEECH, page 254-257. ISCA, (2012)Active Learning by Sparse Instance Tracking and Classifier Confidence in Acoustic Emotion Recognition., and . INTERSPEECH, page 362-365. ISCA, (2012)Affect recognition in real-life acoustic conditions - a new perspective on feature selection., , and . INTERSPEECH, page 2044-2048. ISCA, (2013)Using linguistic information to detect overlapping speech., , , , and . INTERSPEECH, page 690-694. ISCA, (2013)Emotion recognition using imperfect speech recognition., , , , , and . INTERSPEECH, page 478-481. ISCA, (2010)